Tag Archives: Cost savings

Cost-savings is one of the primary objectives for any business intending to maximize the business profitability. Veravizion applies 80:20 rule (and its variants) to help business detect the sources of high fixed and variables costs. The next stage is to identify the levers to reduce overheads and waste in order to effect cost-savings.

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Mar

This article isn’t just about the application of analytics in healthcare. This is more about how analytics is being harnessed to evaluate the latest innovations in healthcare technology in order to help leaders in healthcare make policy decisions about embracing the new technology.

Techno-medical innovations

Over the last couple of decades, there have been quite a few noteworthy technological advancements in healthcare industry. Electronic health records (EHRs), HAART for HIV combined drug therapy, minimally invasive surgery, needle-free injection technology, MRI, genomics, and non-invasive diagnostics are just to name a few. These innovations are extraordinary because they are transforming the way patients are being diagnosed and treated in a better, faster, and safer way.

One such technological advancement was endoscopic surgery or minimally invasive surgery. This innovation revolutionized the way surgeries are performed. Knowledge@Wharton once ranked it tenth among the “Top 30 Innovations of the Last 30 Years” list. The conventional surgical procedures were highly invasive, riskier, painful, and time-consuming. They required long post-operative hospital stays and longer recovery times. Thanks to technological innovations, today, patients have an option of choosing either robotic surgery or endoscopic (non-robotic) surgery, which result in much shorter recovery times, less pain and dramatically reduced scarring. This augurs well for patients who are looking to return back to work quickly.

Minimally Invasive Cardiac Surgery

Many hospitals and cardiac care centers worldwide are evaluating the efficacy of the newer – minimal invasive approach – for specific cardiac surgical procedures. The conventional cardiac valve repair/replacement surgery involved opening up a patient with an 18 -20 cm vertical incision at the sternum. The newer minimally invasive procedure involves approaching the heart through a much smaller (horizontal up to 7cm or a key-hole) incision under the right breast. The new cardiac procedures are more complex for surgeons to perform as the area of access (to heart) narrows down drastically compared to the wider access that the conventional surgery allows. Nevertheless, the new procedure is believed to involve less bleeding, lower risk of infection, faster recovery times, and lesser expenses for patients.

Analytics in Healthcare – A Case Study

Our client# had been studying the effectiveness of the new method of cardiac surgery compared to the conventional way of performing the same procedure. The study allowed them to enroll patients for one of the two types of procedures depending upon a number of physiological and health factors of each patient. They performed the study over a period of three years and recorded the observations. The observed data included the type of procedure performed along with a number of associated output parameters such as hospital stay duration and pain levels experienced, among many others. A power of 80% was chosen; the data was collated and randomized for detailed analysis. Our analysts collaborated closely with the client to understand the nature and significance of each output parameter. We identified the right statistical tools and techniques to be applied based on the nature and type of the data to be analysed. The analyses were performed with a statistical significance level of 5%. The results were examined in detail both for statistical and clinical significance. The statistical test results were cross-checked quantitatively as well as qualitatively with subject matter experts for completeness and correctness in order to arrive at unambiguous conclusions. Each conclusion shared with the client was solidly backed with data. The results would help them make a fact-based policy decision to embrace the newer procedure for their center. Going beyond the statistical analysis, a predictive model was developed based on the results of the initial study. The predictive model would assist the client in determining the right approach to adopt for a future patient depending upon a number of factors. This is expected to improve the cardiac procedural outcome at the healthcare center.

Application of analytics in healthcare

Predictive modeling using machine learning is a powerful technique that helps in forecasting a probable outcome based on empirical data. Predictive modeling and analytics has tremendous potential in healthcare to improve the overall quality of patient care services. Analytics has shown promise to all the constituents involved in the healthcare sector viz. patients, physicians/surgeons, hospitals, pharmaceutical companies, insurance companies, and public health professionals.

Patients – be more aware of self-health

Some of the uses of predictive analytics include increased accuracy of diagnosis, early detection of a disease condition in at-risk patients using genomics, and evidence based medicine. In general, with the proliferation of wearables, patients can be more aware and assured of their own physical conditions.

Physicians/Surgeons – increase diagnostic accuracy

When a patient is visiting a physician complaining chest pain, it is often difficult for the physician to know whether the person needs hospitalization. If the doctor is using a well-tested predictive diagnostic system, in which he can accurately input the patient’s physical and clinical condition, then the system can assist the physician make an informed judgement.

On the treatment side, a physician can follow a patient’s data (or EHRs) for many years and can prescribe a treatment regime tailored to the patient’s specific condition. This fact-based treatment reduces the probability of causing any major side effects.

Hospitals – improve patient care with low mortality rates

Like the case study narrated above, predictive analytics can help hospitals and research centers in evaluating the efficacy of various procedures and treatments in order to improve the mortality and morbidity rates during the post-op period.

Researching a new drug and conducting a clinical trial for the new drug are two very lengthy, costly and resource intensive processes for pharma companies. The R&D process for pharma companies can become more productive by leveraging the power of machine learning to systematically test the mixtures of existing proven molecular components. This may help in identifying new drugs with higher probability for success. Moreover, predictive modeling can be implemented to test the effectiveness of new drugs in a faster and less expensive manner. This will not only help them bring the drug to the market more quickly, but will also reduce the overall healthcare costs per patient significantly.

Insurance Companies – reduce cost of insurance

Healthcare insurance service providers can implement predictive analytics models to better forecast insurance cost for individuals. Presently, the insurance cost is more a function of a person’s age, current medical condition, and the ‘plan’ they are opting for. Now, advancements in medical technology have made it possible to make genetic information and other healthcare related data easily available. Insurance providers can make use of this information to arrive at future medical expenses for a person, and make more informed decisions about the insurance costs associated with that individual. This will be a more realistic assessment of insurance needs for a person and will be beneficial to both sides in terms of provisions to be made.

Public Health (Professionals)

The World Health Organization defines public health as all privately and publicly sponsored measures to prevent disease, promote health, and prolong life among the population as a whole. Its activities aim to provide conditions in which people can be healthy and focus on entire populations, not on individual patients or diseases. Here, analytics can be implemented in predicting early detection of pandemics and flu outbreaks. GoogleFlu was a project which estimated Flu and Dengue fever based on search patterns. While the project is not publishing anymore, empirical data is available for research purposes.

Conclusion

While application of analytics in healthcare is possible in all spheres of patient care, it is more about leveraging the power of analytics in rapidly evaluating the true value of techno-medical innovations for human benefits. Analytics makes it possible to make fact-based decisions about adopting and internalizing these latest technological advancements that promises to help us lead a quality life for a few years more.

Feb

L ast Friday, I was having a very productive give-and-take with a group of business people on the benefits of business analytics. During our conversation, I had an interesting observation about what those people considered the most important thing in analytics. They seemed to think that data is the most important element in any (business) analytics project. I am sharing here the gist of that conversation. Hope you too find it interesting.

I believe data is the second most important thing in analytics or data science.

The most important thing in analytics is ‘the question to be answered’.

Let me explain why.

Typically analytics initiatives are undertaken to achieve one of these four objectives:
1. To solve a problem
2. To accomplish a specific (business or non-business) goal
3. To prove or disprove a hypothesis
4. To answer a question

Unless you have a specific question that needs answered, any analytics initiative will at best be a random analytical study with no direction. The specific question or the problem statement gives it a definite goal. More importantly, the goal will determine what data to use to arrive at a fact-based conclusion.

I like to think of it as ‘a ship sailing in an ocean’ analogy. An analytics initiative making way through the huge organizational data is analogous to a ship sailing in an ocean. If the ship has a pre-determined destination, then it takes the intended route to reach that destination and achieves its objective. To a bystander, mere existence of a ship creates a perception that it is going somewhere. However, if the destination is not clearly defined, the ship might just have a fun time cruising the ocean and figuring out what the ocean has to show. Worse, it might just flounder in the huge ocean and may not reach any meaningful place.

A ship like that is like a holiday-cruise-liner – expensive to sail but not tasked with any objective to reach a place.

Likewise, many organizations launch their analytics initiatives without any specific target. They just start with a tentative goal of let’s figure out what the data shows along the way. In the absence of any pointed objective to pursue, the analysts running the initiative either have great fun cruising with unbounded data or they just lose their way in it. Such an initiative renders no meaningful results.

An analytics initiative like that ends up becoming an expensive proposition like a luxury cruise-liner.

That’s why the most important thing in analytics is the question to be answered. The question determines what data to use to reach a definitive answer.
For example, to determine an organization’s target customer profile and their buying habits, the customer data will need to be analysed. Similarly, the sales history data will be required to ascertain a company’s sales trends.

The data serves as the route to finding the answer to the question.

The data to be analysed will change with the question to be answered. In our analogy, it is the route taken by the ship to its destination. Different destinations will warrant different routes to be pursued.

The selection of the right data becomes important after determining the objective(s) of the analytics initiative. The right prioritization will not only help you reach your destination faster, but it will also be very cost-effective as you save yourself from expending time, money, and manpower in analysing needless data.

In a nutshell, make sure that you do not end up getting lost in the ocean of data with wrong prioritization. Data-cruising is an expensive way to have fun at the expense of an organization’s usually scarce resources.

You can also subscribe to our blog – Our Perspectives – to receive interesting articles and tips in email. We would love to read your perspectives and comments on that.

Dec

A s a CxO considering analytics solutions for your organization, whether to buy or build is invariably the first perplexing question you will face. There are many reports out there that evaluate these options and propose one of them.

For a business, it is of paramount importance to get a system designed and developed that meets the organization’s strategic goals. On the Buy↔Build spectrum, ‘buying a solution and twisting it to force a fit with your requirements’ is rarely an ideal way for most clients to meet their real requirements. On the other hand, it is often costly to ‘build your own thing’ in terms of time, money, and effort.

We would like to look at the buy-or-build decision from a broader perspective.

We believe that this decision depends upon two main questions:
1. Who is making the decision? The size and type of organization making the decision.
2. What is their purpose? The benefits for which the organization is making the decision.

The business needs of a multinational corporation are vastly different from those of a small and medium enterprise (SME) or those of a neighbourhood store. Moreover, the purpose for which such a solution is required by these businesses is also very different. While a large corporation may be willing to invest huge amounts of resources in such an initiative to seek strategic benefits, an SME might just want to implement it as a one-time project with tactical benefits in mind. In short, it depends on what job each business wants to get done. Therefore, we have identified a third option, which is becoming more popular, to acknowledge the real needs that most corporations have but tend to think only in terms of ‘Buy’ or ‘Build’. We call it the ‘Get-the-job-done’ option. So before we delve deeper into the details of buy, get-the-job-done, and build, let us start on the same page in terms of what each of these decisions mean:

A] The ‘BUY’ decision: Buying a product off-the-shelf or buying a license to use a product with little or no customization. In general, the product is licensed by the vendor or a subscription-fee is charged for its recurrent usage.

B] The ‘BUILD’ decision: Setting up an in-house team of technically skilled resources to develop a product yourself from scratch. This decision entails upfront investment in terms of recruiting the right-skilled resources, training them, deploying the necessary technical infrastructure, and having an overhead team of managers to supervise the development efforts to ensure that the product is developed as envisaged.

C] The ‘GET-THE-JOB-DONE’ decision: This decision involves hiring an analytics services provider to achieve your specific requirements, be it strategic or tactical. Such initiatives usually begin as a tactical project but go on to become strategic in nature once the initial results are visible.

In order to make this decision, you need to evaluate your requirements on the following five mutually exclusive but collectively exhaustive parameters:
a. Scale of operations
b. Benefits sought
c. Total cost of ownership (TCO)
d. Availability of resources – skilled people, time, infrastructure
e. Risk tolerance

So let us look at each option in detail.

A] When and why should you consider the BUY decision?

The BUY option should be pursued whenever the focus is on acquiring a product with standard specifications, quickly.

a. Scale of operation is very small. For a small business operating in a neighbourhood, the requirements are usually generic. If you are running a corner-shop, like a coffee shop or a sandwich-bar, some standard products are generally available. These ready-made solutions address most of your broad requirements. Level of customization sought is low. So you can run your business effectively with least modifications in a standard product as it matches most of your needs. Your customization requirements are not high enough to warrant investment for a customized product.
b. Benefits expected are generally (but not necessarily) tactical in nature. A small business, like a fashion accessories corner-shop, is generally looking for a quick turnaround of inventory or brisk sales during festival season. As time is of essence, you do not have the luxury to build a custom solution. In such cases, you should go for a ready-made product.
c. Lack of skilled technical resources may be the key factor in deciding against going for a custom solution.
d. Micro businesses typically have budget constraints. As a result, it is not economically feasible for you to build a proprietary solution. You may find it more logical to buy a cheaper commercial off the shelf (COTS) product instead.
e. Risk tolerance is not high. A couple of wrong decisions might result in big setbacks to a micro-business hence it is always prudent to go for a well-proven solution that does not cost a lot of money.

B] When and why should you consider the BUILD decision?

The BUILD option should be pursued whenever the focus is on addressing the strategic objectives of the large organization and cost is not the immediate concern.

a. Scale of operations is enormous. A multinational corporation with global operations, like a financial institution or a telecom company, generally has numerous product lines targeting multicultural consumers worldwide. These businesses require a solution that has functionalities appropriate to satisfy the disparate needs of their diverse customer segments. So a high level of customization is required by large corporations. COTS products with standard specifications are rigid to modifications and cannot meet these specialized needs. Hence a proprietary solution may be better equipped to address your business scalability concerns.
b. Benefits expected are predictably strategic in nature. As a CEO of a large corporation, like a multinational retail chain or a consumer products company, you are constantly looking for distinct competitive advantage to outperform your rival(s). To gain that edge, you cannot rely on the same off-the-shelf product bought by your competitors; you need an analytical solution that is tailor-made to optimize your business specific processes and operations. Devising your own customized system is likely to give you the competitive advantage in the long run.
c. Availability of resources – people, infrastructure, and time – is pivotal to building a truly productive solution. You must recruit right-skilled people viz. data scientists, analysts, and analytics experts, and train them to form a cohesive team. This team will have to be deftly led by able managers in order to build great analytical systems and tools, in a timely manner. In today’s fast changing world, the development team will have to be agile in incorporating changes in the system to keep with the technological pace in order to outplay the competitors.
d. Total cost of ownership (TCO) in case of custom-built solution is typically high. A large organization going for the build option will need to invest heavily in order to extract the strategic benefits to the fullest. These big companies are able to do so thanks to their deep pockets. That’s why the in-house ‘build’ option is suitable primarily for the large corporations.
e. There is a discreet risk involved in heavy upfront investment. Large corporations deploy an in-house team of developers expecting large gains at a later date. However, many things can go wrong, say, the technology may itself become obsolete, or the actual gains may not be worth the time and efforts. Moreover, in-house analytics team may distract the company away from its focus on the core business. Nevertheless, large cash-rich corporations acknowledge these risks and have a high tolerance to bear them.

C] When should you decide to just GET THE JOB DONE?

This option is fast emerging as the preferred alternative among SMEs and even some large organizations as it takes away the complexity and enables you to compete on analytics in a cost effective way. You should decide to hire analytics service provider whenever the focus of your organization is on getting results – cost-effectively and with lower risk.

a. Size of operation varies between a micro-scale and a global business; small and medium sized enterprises (SME) fall in this category. For an SME operating at a regional or a national level, the business needs vary tremendously because the target customer segments differ a lot. An off-the-shelf product that is rigid to modifications is invariably unsuitable for your needs. A ready-made product only means you have to forcefully fit your requirements to the features provided by it. Hence buying an off-the-shelf product is not a sensible approach because you do not want to end up paying your hard earned money for features your business does not want. If you are running a small or medium sized enterprise, like a retail furniture store, a retail consumer goods shop, or a B2C services company, level of customization required by your business is quite high. You need a solution that is flexible to frequent changes. Therefore, an analytics partner is ideal for you to help meet your true needs.
b. Benefits are expected quickly and in a cost-effective manner as the focus is to win quick results. As a business leader running an SME, you want to keep your focus on your core business. You really need a partner that provides flexibility, gives option to customize your requirements, and works for your success while ensuring good customer service.
c. Total cost of ownership (TCO) is comparatively low if you are employing an analytics service provider. They will offer customer service in deploying the solution and training your staff, so you save on the maintenance and training costs otherwise incurred in case of buying or building a product. That’s how the get-the-job-done option reduces your total cost of ownership.
d. Resource requirement is minimal for you if you go for analytics services partner. If you do not have a team of skilled resources to develop your own analytics solution, then it would be a wise decision to hire an analytics partner who will have expertise to productively work towards achieving your business objectives.
e. You can choose to share the risk with your analytics partner by opting to outsource your organization’s analytics activities. The get-the-job-done decision is attractive for its feature of sharing the risk between the business and the analytics partner.
Having said thus, it is not written in stone that an SME cannot build its own custom solution or a micro-business cannot opt for analytics services option. There are no stringent rules as such. The following table only illustrates a one-glance view of the merits and demerits of each of these options for most cases. In a nutshell, the decision of whether to buy or build or get the job done depends on your specific requirements and your preference for each of these options.

You can also subscribe to our blog – Our Perspectives – to receive interesting articles and tips in email. We would love to read your perspectives and comments on that.

Oct

T raditionally, universities in the U.S. have earned their revenues through state (government) funding, tuition and fees, research and development grants, returns from endowments, and philanthropic donations.

Funding and ROI Challenges

The prolonged cash-crunch due to the long-winded recession of 2008 forced the policymakers to make some nearsighted (and some say misguided) policy changes like cut in public spending on higher education. The policy changes caused steady decline in state funding to universities and many universities found themselves grappling with financial sustenance. The slump in economy also caused research grants and philanthropic donations to shrink. In the face of volatile and unreliable nature of returns from endowments, universities were compelled to shift the burden of additional costs to students in the form of increased tuition and fees.

Universities in the U.K. too are facing similar funding challenges. According to the data published by Higher Education Statistics Agency (HESA) and the Universities UK (UUK), the public spending in U.K. on higher education reduced by 6.9% post the recession. Over the last few years there has been a reduction in the proportion of income from funding body grants to total income. The funding from Higher Education Funding Council for England (HEFCE) is expected to reduce further. The recession has also contributed to a large decrease in the ratio of research income from research grants and contracts. Nevertheless, the past three years have seen relative stability in terms of the total amount of money flowing to institutions. HESA data also points to 38.6 per cent decrease in endowment and investment income over 2009-10 and it decreased by 25 per cent across the U.K. over the period 2000-01 to 2009-10. Gradually, the universities in U.K. too increased tuition and other fees to cover for the costs. One major difference between universities in U.S. and U.K. is that, the private expenditure on higher education is much greater than public expenditure in the U.S. universities.

Overall, the balance between the funding grants, and tuition and fees is moving towards fees, a trend seen in the U.S. universities too. Gradually, the rise in tuition and other fees are becoming unsustainable, especially for postgraduate students, already under huge debt from undergraduate studies. Exhibit 1 shows the comparative trend between changes in college tuition and fees vis-à-vis the changes in the cost of all consumer items in the U.S.. Starting at the same level in 1978, the tuition and fees cost seems to have increased five-fold as compared to consumer prices over the last few decades.

Moreover, the rates at which people’s incomes have gone up have not been able to catch up with this high rate of increase in college fees. The rate of change of college tuition has overshadowed the inflation rate consistently since 1981 as shown in exhibit 2. Considering that students join higher education primarily for higher pay packages, this unsustainable rise in the cost of college fees in the face of high unemployment is impacting universities’ enrolments adversely.

From the perspective of the universities, the costs are steadily increasing. The costs such as faculty salaries, college infrastructure budgets, administrative expenses, IT infrastructure costs, and marketing overheads form the fixed costs that are incurred irrespective of any student taking admission. If the number of students enrolled goes down, the average cost per student goes up. This situation makes it unsustainable to run the famed institutions delivering the same level of quality. This results in pressure to achieve surplus funds, after accounting for staff, administration, and operating expenses. While the universities in the U.K. have managed to achieve a surplus in the last couple of years, it is not before raising the income from other services rendered such as, residences and catering operations, grants from local authorities, income from health and hospital authorities, and income from intellectual property rights.

So how can universities respond?

Universities can take a series of steps that will help them stand-up to these multi-dimensional challenges to save costs and increase incomes.

Firstly, in order to save costs, university leaders need to improve productivity on teaching related activities. This can be done by rationalizing the programs and courses offered, integrating departments to normalize instructional costs, streamlining operational processes to leverage synergies, and outsourcing non-instructional activities to specialized vendors. Many universities accept that the programs and courses offered by them have experienced proliferation over the period of time. Thus, merging similar programs will not only make them more effective but also save costs for the colleges. On the same lines, integrating departments and streamlining operational processes that are similar in nature will help making the operations leaner, faster, and cost effective. Most importantly, institutions must focus on providing value in the area of their competitive advantages. This entails outsourcing non-core activities to reduce non-instructional expenditure. Analytics can come very handy in helping to understand the potential levers to realize savings from taking up these activities.

Secondly, ensuring enrolments of the ‘right’ students will pave ways for improving incomes. In addition to bringing tuition and fees, the right students can help make the universities look good through their research and development activities, which in turn will help the institutions to attract funding from existing and other green-field sources. One big aspect of improving income is to retain existing students from dropping out. High drop-out rates has become an area of serious concern for many universities. Reasonably so, every dropped-out student creates a hole in tuition fees that invariably remains unfilled. Moreover a high drop-out rate impacts twice, in lost revenues and sunk costs. Therefore, student retention should be looked-at through the same lens as businesses look at customer churn, and earnest measures should be implemented to control the high drop-out rates.

To summarize, universities need to take unconventional actions to effectively overcome the funding challenges and strive to be lean in order to embrace the opportunities of the future.